Measure Based Regularization
Olivier Bousquet, Olivier Chapelle, Matthias Hein · 2003
We address in this paper the question of how the knowledge of the marginal distribution $P(x)$ can be incorporated in a learning algorithm. We suggest three theoretical methods for taking into account this distribution for regularization and provide links to existing graph-based semi-supervised learning algorithms. We also propose practical implementations.